标题： 一种用于线性混合效应模型的特征选择松弛方法 显示英文标题

标题： A Relaxation Approach to Feature Selection for Linear Mixed Effects Models

摘要： 线性混合效应（LME）模型是建模相关数据的基本工具，包括队列研究、纵向数据分析和元分析。 LME变量选择方法的设计和分析比线性回归更困难，因为LME模型是非线性的。 在本工作中，我们提出了一种放松策略和优化方法，使得可以使用凸和非凸正则化方法对LME进行广泛变量选择，包括$\ell_1$，自适应-$\ell_1$，CAD，以及$\ell_0$。 计算框架只需要每个正则化方法的近端算子可用，实现可在开源Python包pysr3中获得，符合sklearn标准。 在模拟数据集上的数值结果表明，所提出的方法在准确性和计算时间方面均优于现有技术。 变量选择技术也在一个实际例子中得到验证，使用的是关于欺凌受害的数据集。 显示英文摘要

摘要： Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this work we propose a relaxation strategy and optimization methods that enable a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including $\ell_1$, Adaptive-$\ell_1$, CAD, and $\ell_0$. The computational framework only requires the proximal operator for each regularizer to be available, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization.